Cerebral blood flow dynamics: Is there more to the story at exercise onset?

Abstract A monoexponential model characterizing cerebral blood velocity dynamics at the onset of exercise may mask dynamic responses by the cerebrovasculature countering large fluctuations of middle cerebral artery blood velocity (MCAv) and cerebral perfusion pressure (CPP) oscillations. Therefore, the purpose of this study was to determine whether the use of a monoexponential model attributes initial fluctuations of MCAv at the start of exercise as a time delay (TD). Twenty‐three adults (10 women, 23.9 ± 3.3 yrs; 23.7 ± 2.4 kg/m2) completed 2 min of rest followed by 3 mins of recumbent cycling at 50 W. MCAv, CPP, and Cerebrovascular Conductance index (CVCi), calculated as CVCi = MCAv/MAP × 100 mmHg, were collected, a lowpass filter (0.2 Hz) was applied, and averaged into 3‐second bins. MCAv data were then fit to a monoexponential model [ΔMCAv(t) = Amp(1 – e−(t−TD)/τ)]. TD, tau (τ), and mean response time (MRT = TD + τ) were obtained from the model. Subjects exhibited a TD of 20.2 ± 18.1 s. TD was directly correlated with MCAv nadir (MCAvN), r = −0.560, p = 0.007, which occurred at similar times (16.5 ± 15.3 vs. 20.2 ± 18.1 s, p = 0.967). Regressions indicated CPP as the strongest predictor of MCAvN (Ra2 = 0.36). Fluctuations in MCAv were masked using a monoexponential model. To adequately understand cerebrovascular mechanisms during the transition from rest to exercise, CPP and CVCi must also be analyzed. A concurrent drop in cerebral perfusion pressure and middle cerebral artery blood velocity at the start of exercise forces the cerebrovasculature to respond to maintain cerebral blood flow. The use of a monoexponential model characterizes this initial phase as a time delay and masks this large important response.


Abstract
A monoexponential model characterizing cerebral blood velocity dynamics at the onset of exercise may mask dynamic responses by the cerebrovasculature countering large fluctuations of middle cerebral artery blood velocity (MCAv) and cerebral perfusion pressure (CPP) oscillations. Therefore, the purpose of this study was to determine whether the use of a monoexponential model attributes initial fluctuations of MCAv at the start of exercise as a time delay (TD). Twenty-three adults (10 women, 23.9 ± 3.3 yrs; 23.7 ± 2.4 kg/m 2 ) completed 2 min of rest followed by 3 mins of recumbent cycling at 50 W. MCAv, CPP, and Cerebrovascular Conductance index (CVCi), calculated as CVCi = MCAv/MAP × 100 mmHg, were collected, a lowpass filter (0.2 Hz) was applied, and averaged into 3-second bins. MCAv data were then fit to a monoexponential model [ΔMCAv(t) = Amp(1 -e −(t−TD)/τ )]. TD, tau (τ), and mean response time (MRT = TD + τ) were obtained from the model.
Subjects exhibited a TD of 20.2 ± 18.1 s. TD was directly correlated with MCAv nadir (MCAv N ), r = −0.560, p = 0.007, which occurred at similar times (16.5 ± 15.3 vs. 20.2 ± 18.1 s, p = 0.967). Regressions indicated CPP as the strongest predictor of MCAv N (R 2 a = 0.36). Fluctuations in MCAv were masked using a monoexponential model. To adequately understand cerebrovascular mechanisms during the transition from rest to exercise, CPP and CVCi must also be analyzed. A concurrent drop in cerebral perfusion pressure and middle cerebral artery blood velocity at the start of exercise forces the cerebrovasculature to respond to maintain cerebral blood flow. The use of a monoexponential model characterizes this initial phase as a time delay and masks this large important response.

| INTRODUCTION
Analyzing the kinetics of physiological responses to exercise has allowed for greater understanding and new insights related to oxygen consumption (VO 2 ), oxygenation, and peripheral blood flow control in dynamic settings (Barbosa et al., 2011;Poole & Jones, 2013;Tschakovsky et al., 2006). Recently, a monoexponential model has been applied to analyze middle cerebral artery blood velocity (MCAv) data from a rest-to-exercise transition using various large muscle mass exercise protocols (Billinger et al., 2017;Weston et al., 2022). Since then, several studies published using this analysis technique comparing various populations suggest differences exist with time delay (TD) in health and disease (Kaufman et al., 2019;Ward et al., 2018Ward et al., , 2022Weston et al., 2022;Witte et al., 2019). These data are important to gain a greater understanding of the dynamic adjustment of MCAv. However, several of the data put forth warrant more investigation to better understand what these differences mean and how to properly interpret these results.
When MCAv data from a rest-to-exercise transition are fit with a monoexponential model, the TD for young healthy adults is reported to be 30-50 s from the start of exercise to the model-detected increase in MCAv (Billinger et al., 2017;Ward et al., 2018;Weston et al., 2022). It is unlikely the cerebrovasculature is less responsive (TD ~ 10 s) than other systems (Craig et al., 2021;MacDonald et al., 1998;Saunders et al., 2005;Tschakovsky et al., 2006) given the tight flow-metabolism coupling within the brain (Sheth et al., 2004;Willie et al., 2014). A more reasonable assumption is the oscillations of MCAv at the start of exercise are characterized as a TD when using a monoexponential model. As first pointed out by Weston et al. (2022), this initial fluctuation lasts around 25 s. They speculated this was a result of changes pressure and end-tidal CO 2 (EtCO 2 ). Both CO 2 and pressure are strong vasoactive stimuli within the cerebrovasculature (Caldwell et al., 2021;Poole & Jones, 2013;Willie et al., 2014). However, given the exercise-induced hypotension at the start of large muscle mass exercise, (Craig et al., 2021;Ogoh et al., 2022;Wieling et al., 1996), and the synchronization between pressure and MCAv (Favre & Serrador, 2019;Kay & Rickards, 2016;Labrecque et al., 2019), we believe pressure is the primary contributor to the initial MCAv fluctuations, and therefore, the extended TD being reported.
Therefore, the purpose of this study was to analyze the initial oscillations observed with MCAv recordings at the start of cycling exercise and determine whether these oscillations are a result of a transient exercise-induced hypotension. We hypothesized that at the beginning of exercise, MCAv will show a transient but significant drop prior to a monoexponential increase. We also hypothesized that this drop is attributable to a concurrent drop in cerebral perfusion pressure (CPP).

| Participants
Thirty-two young (18-30 years; 16 Females, 16 Males), healthy (BMI < 30 kg/m 2 ) subjects were recruited for the current study. Graded-exercise testing data were previously published (Ashley et al., 2020). Two subjects (1 female and 1 male) were removed due to equipment malfunction, and 7 subjects (5 females and 2 Males) were removed due to non-exponential increase, or model failure (see MCAv Kinetic Analysis). Therefore, 23 subjects (10 females and 13 males, Table 1) were analyzed in the present study. All subjects reported being free of cardiovascular, metabolic, or respiratory diseases, physical ailments, and were not considered sedentary (>600 MET-min/wk) as determined by a medical history questionnaire and the International Physical Activity Questionnaire (iPAQ) long form. Females with a normal menstruating cycle were studied within the early follicular phase (1-7 days) of their menstrual cycle or during the placebo phase of their oral contraceptives to minimize gonadal hormone influences on vasculature (Hashimoto et al., 1995). All study procedures were approved by the Institutional Review Board at the University of Oklahoma Health Sciences Center (IRB# 10121) and conformed to the standards set by the Declaration of Helsinki with the exception of registration in a database.

| Protocol
All participants completed two laboratory visits. For both visits, participants were asked to arrive ≥8 h fasted, ≥12 h without caffeine, and ≥24 h without exercise, alcohol, or the use of supplements or nonsteroidal anti-inflammatory drugs. On the first visit, participants provided written informed consent and completed medical history and iPAQ questionnaires. Following consent, participants' height (Novel Products, Inc.), weight (BWB-800A, Tanita), resting blood pressure, and a venous blood sample were collected. Resting blood pressure was measured following ≥5 mins of supine rest using an automated upper arm cuff (HEM-705, Omron). Blood was analyzed (CardioCheck PA, Poymer Technology Systems Inc.) for fasting glucose <100 mg/dL, triglycerides <150 mg/dL, HDL ≥40 mg/dL in males or ≥50 mg/dL in females for inclusion. Participants were then familiarized with the study equipment and procedures, and a quality transcranial doppler (TCD) signal of the MCA was confirmed. On the second visit, participants arrived at the laboratory, as previously requested, were fitted with the study equipment and sat quietly on a recumbent cycle ergometer (Lode Corival cpet). After at ≥2 min of quiet rest (Baseline), subjects immediately began peddling at 50 W between 60 and 80 RPM 3 min.

| Data analysis
Data were exported in raw format (20 kHz), and a lowpass filter (set at 0.2 Hz) was applied (Ferreira et al., 2006). These data were then averaged into 3-s time bins for comparative analysis (Billinger et al., 2017). Both left and right MCA were insonated and averaged together to form a single response. For twelve subjects, only one MCA was insonated, that side was used for data analysis. The last 30 s prior to the start of exercise was averaged for baseline. CPP was calculated as [CPP = MAP -(0.7355 × height in cm from heart to TCD probe)] to account for the hydrostatic column (Deegan et al., 2010;Des, 2012;Favre et al., 2020). Cerebral vascular conductance index (CVCi) was calculated as (CVCi = MCAv/CPP × 100 mmHg). Several points of interest were identified and used for analysis. Immediately at the start of exercise, both MCAv and CPP dropped to a nadir (MCAv N , CPP N ), and CVCi increased towards a maximum (CVCi M , see Figure 1). To assess the ability of cerebral autoregulation to maintain CBF during the rapid changes in perfusion pressure at the onset of exercise, autoregulatory compensation index (ARCi) was calculated as

| MCAv kinetic analysis
The rest-to-exercise MCAv transition data were fit with a monoexponential model using SigmaPlot 12.5 (Systat Software, Inc.) as previous research has shown (Billinger et al., 2017;Witte et al., 2019). The model used was the following: where ΔMCAv(t) is the change in MCAv from baseline to any given timepoint, BL is the baseline prior to exercise start (data are baseline corrected; therefore, baseline is set to 0), Amp is the change from baseline to the steady-state MCAv, TD is the time prior to an increase in MCAv, and tau (τ) is the time constant from the response to reach 63% of steady state. Mean response time (MRT) was calculated by adding TD and tau. This gave a value representing the total time from the start of exercise, until MCAv reached 63% of the steady-state value (Phillips et al., 1995). Model fit was assessed through visual inspection of the model, R 2 a value, and equivalence of variance in residuals, tested using SigmaPlot's Constant Variance Test which utilizes a Spearman rank correlation between the absolute values of the residuals and the observed value of the dependent variable. If it was found that a given data set had unequal variance in residuals, it was considered a "model failure" and removed from analysis. ΔMCAv(t) = BL + Amp 1 − e −(t−TD)∕τ F I G U R E 1 Change from rest (set to 0) to immediate 50 W cycling exercise for middle cerebral artery blood velocity (MCAv; a), cerebral perfusion pressure (CPP; b), cerebrovascular conductance index (CVCi; c), and end-tidal CO 2 (EtCO 2 ; d). Exercise start is indicated by the vertical dashed lines. All data sets presented are averaged 3-s bins and from representative subjects. Solid line on graph A represents best fit using a monoexponential model. Adjusted R 2 (R 2 a ) = 0.61 ± 0.14. Standard Error of Regression (S) = 3.74 ± 0.91.

| Statistical analysis
The cerebral hemodynamic responses (MCAv N , CPP N , and CVCi M ) were correlated with model characteristics (TD, tau, and MRT) to determine their contributions to changes in model results. Two-tailed dependent samples t-tests were used to determine whether MCAv N occurred at similar times to CPP N , CVCI M , and TD. If any of the t-tests revealed no significance (p > 0.05), a two one-sided test (TOST) was completed to determine whether the means were equivalent (Lakens, 2017). Using a sample size of N = 23, α = 0.05, and power of 80%, the smallest effect size of interest was calculated as d = 0.611, which was used in the TOST procedure. A more in-depth description of this type of analysis and its results can be found elsewhere (Lakens, 2017). Briefly, TOST calculates two p values based on two one-sided t-tests. This test determines whether the difference between the two samples is greater than an equivalence boundary based on the effect size. If both t-tests are significant (i.e., both p values are below 0.05), the data are considered equivalent. Only the largest p value of the two tests is reported, as follows: This means, if the p value reported is significant, the data are considered equivalent. There are scenarios where significance test and TOST results are at odds. We will follow the recommendations as outlined here (Lakens, 2017).
The following regression was calculated to describe MCAv N : where each variable is the change from baseline to the timepoint MCAv N occurred. Each variable was chosen due to previous research showing a strong relationship with CBF (Markwalder et al., 1984;Ogoh et al., 2005). Due to the results of the regression, a follow-up regression with CPP @MCAvN only was completed.
Additionally, retrospective analysis of the data revealed a large distribution of subjects experienced a rapid MCAv response ( Figure S1) as the monoexponential model detected the first data point used in analysis (at 1.5 s) as the increase. Therefore, these subjects were separated into "fast" (TD ≤ 1.5 s) and "slow" (TD > 1.5 s) groups. Model characteristics were compared between the groups using a Welch's t-test. TOST was completed for any non-significant results (p > 0.05). With two samples, N = 8 and N = 15 (fast and slow groups, respectively), α = 0.05 and Power set to 80%, the smallest effect size of interest was calculated as d = 1.32, which was used in the TOST procedure.
Lastly, although the purpose of this study was not to determine whether sex differences exist in cerebral hemodynamics, the nearly-even split between males (N = 13) and females (N = 10) allowed us the opportunity to explore this. A TOST equivalence test was used to determine whether full statistical analysis was needed to determine differences between the sexes (O'Brien & Kimmerly, 2022). Considering the sample size and given a power of 80% with an α of 0.05, an effect size of d = 1.26 would be needed to determine differences between the sexes.
Statistical analyses were completed using R (R Core Team, 2022) software. All regressions (including the monoexponential model) are reported with adjusted R 2 (R 2 a ) to eliminate as much bias as possible and to be used for comparisons across various regression types (Akossou & Palm, 2013), and standard error of the regression (S). All TOST results are reported as t(df) = test statistic, p, where the test statistic and p value reported are the largest of the two t-tests (Lakens, 2017). All data are reported as means ± SD with significance set at p < 0.05.

| Study participants
Characteristics of 23 subjects (10 females) are presented in Table 1. The work rate (50 W) equated to 28.1 ± 6.9% of their max (187.0 ± 39.4 W) output or 37.5 ± 8.3% VO 2Peak which is considered light intensity exercise (American College of Sports Medicine et al., 2021).

MCAv N
To explain MCAv N , a regression of parameters occurring at MCAv N was completed (See methods for more detail). The results of this regression are presented in Table 4.
This model indicates only CPP and CVCi were significant predictors of MCAv N at p < 0.05. However, due to the way CVCi is calculated (CVCi = MCAv/CPP), it will inherently be related to MCAv and presents a mathematical issue for interpretation. Therefore, we completed a regression with CPP @MCAvN as the only predictor (Figure 2a). The overall fit of this model compared to the larger model was Additionally, and mostly for comparative purposes, the same regression was completed using the percentage change values (Figure 2b). The calculated ARCi was 3.5 ± 16.8%.

| Slow responders had greater MCAv fluctuations
The overall response of MCAv to 50 W cycling was not different between the two groups (11.6 ± 6.4 vs. 15.5 ± 4.8 Δcm/s, p = 0.143, slow vs. fast) and tended to be equivalent (t(21) = 1.493, p = 0.0751), whereas the overall response of CPP was greater in the fast group (8.6 ± 8.6 vs. 17.3 ± 8.4 ΔmmHg, p = 0.030, slow vs. fast). Model characteristics of both groups are presented in Table 5.

| DISCUSSION
The principal finding of the current study is that during a rest-to-exercise transition, MCAv briefly drops, which alters the dynamic variables described by a monoexponential model. Our data showed that MCAv N was negatively correlated with TD (r = −0.560) and that MCAv N was well predicted by CPP N@MCAvN only (R 2 a = 0.25, S = 4.74) compared to the full model (R 2 a = 0.36, S = 4.97). This suggests that CPP has a direct impact on MCAv N, which can alter  the TD reported within a monoexponential model. During this time, CVCi rapidly increases, reaching its peak after TD (46.9 s vs. 20.2 s), and then settles near baseline for the remainder of the bout. These data indicate that large cerebral vascular responses are occurring shortly after the start of exercise, and the use of a monoexponential model masks this and reports it as a TD.

| Monoexponential MCAv responses
In agreement with previous studies, we report a monoexponential model displayed a good fit to MCAv restto-exercise transition data (R 2 a = 0.61) which is similar to Billinger et al. (2017) who reported an R 2 = 0.82. The difference can likely be attributed to a few methodological differences between our study and Billinger's. We chose to report R 2 a instead of R 2 to account for bias and improve comparison (Akossou & Palm, 2013), and we used a 0.2 Hz lowpass filter for our data whereas Billinger et al. (2017) used a shape-preserving, piecewise cubic interpolation. Billinger et al. also chose to increase subject's work over 30 s to achieve the desired work rate on a recumbent stepper, whereas subjects in our study began peddling on a recumbent cycle ergometer from rest immediately at 50 W. Together, these data indicate that a monoexponential model is a good tool for MCAv transition analysis. However, there is the unresolved issue of an extended TD. Previous studies have reported a TD of 30-50 s when using moderate-to-high intensity recumbent stepping (Billinger et al., 2017;Ward et al., 2018) and upright cycling (Weston et al., 2022) in young healthy adults. We show TD was negatively correlated with MCAv N (r = −0.560), indicating a longer TD was related to a larger drop in MCAv (i.e., a more negative MCAv N ). Therefore, to explain the extended TD, we need to explain why MCAv reaches a nadir at the onset of exercise.

| Fluctuations in MCAv
We are not the first to report on the initial fluctuations in MCAv at the start of exercise. Weston et al. (2022) seem to have been the first group to shed light on this occurrence. During their monoexponential analysis, they chose to only model the exponential increase due to the oscillations occurring at the start of exercise. Although they did not directly attempt to describe this initial phase, they pointed at pressure and EtCO 2 as primary factors in MCAv oscillations given the cerebrovascular responses to changes in EtCO 2 (Hoiland et al., 2019) and pressure (Favre & Serrador, 2019;Kay & Rickards, 2016;Labrecque et al., 2019;Ogoh et al., 2022). We attempted to answer this question within the constraints of the study protocol. To do this, we applied a multiple regression to MCAv N (Table 4). Within this model, EtCO 2@MCAvN was used and was not a significant predictor of MCAv N . In human cerebral vessels, CO 2 is a potent vasodilator (Smith & Ainslie, 2017;Willie et al., 2014). However, EtCO 2 values only increased by 5.73 ± 2.64 ΔmmHg during 50 W recumbent cycling (Figure 1d). It is unlikely that MCA diameter changed much, if any, at these low values (Verbree et al., 2014). Additionally, given the exercise-induced hypotension (Figure 1b), cerebrovascular reactivity may be altered and therefore complicates the relationship (Willie et al., 2014). We cannot expand on this topic further; however, more testing to specifically answer this relationship is an interesting topic to broach.
The only significant predictor of MCAv N , that did not contain MCAv in its mathematical definition, was CPP @MCAvN (Table 4). Therefore, we performed a linear regression with CPP as the only predictor ( Figure 2a). These regressions had a similar fit (AIC = 140.4 vs. 140.7, Full model vs. CPP only) and were able to explain a similar amount of variance in MCAv N (R 2 a = 0.36 vs. 0.25, Full model vs. CPP only). Pressure displaying the strongest relationship with MCAv at exercise onset is similar to previous research Saito et al., 2022). Although historically Q is thought to be one of the strongest determinates of CBF (Ide et al., 1998;Meng et al., 2015;Ogoh et al., 2005), Saito et al. (2022) found this is only true when analyzing the overall change from baseline to steady state. When analyzing the initial change from baseline to the first 40 s of 20 W cycling exercise, MAP was the strongest correlate . Additionally, Deegan et al. (2010) found that in the presence of dynamic CPP changes, Q was not a major contributor to changes in MCAv. Taken together, with the present data, we can conclude that as pressure drops at the start of exercise, MCAv is brought down with it. This drop in MCAv is not described by the monoexponential model, but instead attributes it to a TD.

| Hypotensive response to exercise
The exercise-onset hypotension we observe is not novel. A seminal study by Wieling et al. (1996) described a large drop in pressure 12 s after upright (−21 mmHg) and supine (−12 mmHg) 50 W cycling (Wieling et al., 1996). Similarly, we observed that CPP N (−13.5 mmHg) occurred 10.3 s after 50 W recumbent cycling onset. Changes in TPR at the onset of exercise are likely the main contributor. Saito et al. (2022) noted systemic vascular resistance reduced towards a nadir at the start of 20 W upright cycling exercise. Recent work has suggested that skeletal muscle activation reduces TPR through rapid vasodilation (Michelini et al., 2015;Tschakovsky & Sheriff, 2004) and through loading of the atrial baroreflex from the skeletal muscle pump (Barbosa et al., 2015;Katayama et al., 2020;Tschakovsky & Sheriff, 2004). The magnitude of the drop in pressure is likely intensity-driven. Although it does not appear to have been directly analyzed, Figure 4 in Barbosa et al. (2016) demonstrates an increased variability and lower nadir in MAP following moderate-intensity exercise compared to low-intensity exercise.
The dramatic and sudden drop of MAP at the start of exercise has a direct effect on MCAv. By activating the metaboreflex through occlusion of the forearm following handgrip exercise, Ogoh et al. (2022) attenuated the drop in MAP and MCAv following 20 W cycling exercise onset. This indicates that the atrial baroreflex alters sympathetic outflow. In their study, they found the relationship between the percentage drop in MCAv and the percentage drop in MAP to have an R 2 = 0.28, similar to the current study (R 2 a = 0.31; Figure 2b). These combined actions (pressure and MCAv dropping) would, therefore, increase the calculated TD, indicating that the calculated TD is intensity-driven. However, not all studies agree that exercise intensity alters TD. Recent evidence directly comparing moderate (80 W) to heavy intensity (159 W) MCAv kinetic data showed that TD values were similar (29.3 vs. 29.1 s) in young healthy adults (Weston et al., 2022) and in middle-aged adults using similar workloads (91 W vs. 115 W) the recorded TDs were not different (43.5 vs 43.8 s) (Witte et al., 2019).

| Cerebrovascular response
During the exercise-onset hypotensive response, cerebral autoregulation would likely cause the cerebrovasculature to vasodilate to maintain flow (Koller & Toth, 2012;Payne, 2016). In the present study, we demonstrate this with CVCi (Figure 1c), an estimate of cerebral vasodilation and vasoconstriction (Burley et al., 2021;Claassen et al., 2007). At exercise onset, CVCi rapidly increases and reaches a peak, after MCAv N (46.9 vs. 16.5 s), then declines and remains near baseline throughout the remainder of the exercise bout (Figure 1c). Not only does this highlight the necessity to focus on more than the steady state, but it also demonstrates the principal motivation for this study. Attributing the initial oscillations to a TD, as is the case with the current monoexponential model, masks the largest and most dynamic response of the cerebrovasculature that occurs during a rest-to-exercise transition. As MCAv and CPP move towards nadirs, this activates various vascular mechanisms that result in vasodilation to maintain flow (Koller & Toth, 2012). We attempted to quantify the ability of the cerebrovasculature to maintain MCAv during the drop in CPP, by calculating ARCi. Had CVCi not changed from baseline (CVCi BL ), the MCAv would have dropped further (Chaudhry et al., 2023). ARCi quantifies how much autoregulation compensates for the drop in CPP to maintain MCAv. In the present study, we calculated ARCi to be 3.5%. This means that cerebral autoregulation was able to mitigate MCAv from dropping a further 3.5%. Although this does not seem very high, it is important to note that the cerebrovasculature maintains a more passive autoregulation in response to sudden drops in pressure as compared to increases in pressure (Claassen et al., 2021;Tan, 2012).

| Fast vs. slow responders
Our data suggest that the subjects may be separated into two groups, "fast" and "slow," when using a monoexponential model to fit the data. Several subjects displayed a rapid increase in MCAv (Figure 3a) at exercise onset (fast, N = 8) and others a more "typical" response (slow, N = 15). These subjects were of similar fitness levels and had similar resting MCAv values. The main difference demographically was that the slow group was more physically active (5549 vs. 2714 MET-min/wk). Their TD values (30.2 vs. 1.5 s, slow vs. fast) and MCAv N (−7.3 vs. 2.0 Δcm/s) were different between the groups, aiding in the argument that TD is being driven by large fluctuations in MCAv. It is important to note that the recorded MCAv N for the fast group is a positive number (2.0 ± 2.6 Δcm/s). This is because for most fast responders, MCAv increases nearly instantly, and the "lowest value" being recorded for these individuals is during the exponential increase. We continue to use the term "nadir" for this group primarily for comparison purposes.
In Figure 3a, a "spike" can be observed in both groups, occurring just after MCAv N for the slow. This is likely a result of the well documented Q "spike" or "overshoot" that occurs at the start of submaximal exercise Wieling et al., 1996). As large-muscle mass, upright exercise starts, Q increases reaching its peak at around 20 s, then reduces and is adjusted until it settles into a steady-state Wieling et al., 1996). At the start of exercise, the skeletal muscle pump loads the atrial baroreflex, resulting in reduced sympathetic function (Katayama et al., 2014). Saito et al. (2022) speculated this may indirectly protect the cerebral circulation by dampening the effects of Q increasing rapidly at the start of exercise. They also note that individuals with altered sympathetic control could have "abnormal CBF responses to the onset of exercise." Although CPP N between the groups was not statistically different (−16.1 vs. −8.7 ΔmmHg, p = 0.106), they were not equivalent (t(21) = 1.326, p = 0.0995), suggesting had there been a larger sample of fast subjects we may have found CPP N to be different between the groups. If CPP N was greater in the fast group, it also appears they had a greater "spike" in MCAv at the start of exercise (Figure 3a). These groups differed in physical activity and overall CPP response (8.6 vs. 17.3 ΔmmHg). Elevated exercising pressure has been linked to various cardiovascular disease risks (Dipla et al., 2017). We must be cautious in drawing conclusions with these data as it was purely exploratory and underpowered. However, the MCAv response in the fast group seems to be novel. The possibility that several young adults that are healthy, free of any cardiometabolic disease had what appears to be an abnormal MCAv response to exercise highlights the importance of examining the initial fluctuations in MCAv.

CONSIDERATIONS
We have attempted to limit confounding variables to the best of our ability; however, we acknowledge that there are several methodological considerations for this study. First, all female participants in our study were observed during the early follicular phase of the menstrual cycle to reduce possible cardiovascular hormonal influences (D'Urzo et al., 2018;Kellawan et al., 2015;Krejza et al., 2003;Limberg et al., 2010;Parker et al., 2007) even though there is a lack of evidence that menstrual cycle phase effects cerebrovascular control (Favre & Serrador, 2019;Krejza et al., 2003). We opted for a cautious approach to reduce any possibility that menstrual cycle phase would complicate data interpretation.
Secondly, TCD measures blood flow velocity through the MCA and does not account for vessel diameter. Therefore, our study did not directly measure volumetric blood flow. The cross-sectional area (CSA) of conduit cerebral vessels will change in response to hand-grip exercise and changes in arterial gases (Kellawan et al., 2016(Kellawan et al., , 2017Verbree et al., 2014Verbree et al., , 2017. However, whether or not the MCA CSA changes during large muscle mass exercise remain unclear as cerebrovascular mechanisms responding to changes in perfusion pressure and arterial gases would be in opposition to each other (Smith & Ainslie, 2017). However, the values of EtCO 2 observed in this study and the light exercise intensity would result in negligible changes to MCA CSA (Verbree et al., 2014(Verbree et al., , 2017. Conversely, more advanced imaging techniques such as MRI would not provide a temporal resolution required to answer the research questions addressed in this investigation (Wåhlin et al., 2013). Therefore, despite these limitations, we would argue that TCD was the superior instrument providing a surrogate for MCA blood flow during exercise.
Additionally, the data analyzed and expressed here are based off one exercise bout. Previous studies analyzing MCAv with a monoexponential model typically have used three repeated bouts in order to reduce signal-tonoise ratio (Billinger et al., 2017;Weston et al., 2022). This is not universal, and monoexponential modeling of a single-bout of MCAv rest-to-exercise transition data has been done previously (Ward et al., 2018;Witte et al., 2019). The concept of averaging multiple bouts is done specifically to account for breath-by-breath variations (Poole & Jones, 2013;Whipp et al., 1982). It is unclear if the same technique is required of nonventilatory data. However, we do recognize that averaging of several bouts would reduce variability and likely increase understanding.

| CONCLUSION
This study found that TD is directly correlated with MCAv N and fluctuations of MCAv at the start of exercise are likely a result of an exercise-onset hypotension reducing CPP. Cerebral autoregulation suggests that the cerebral vasculature responds to MCAv and CPP declining towards a nadir by rapidly dilating to maintain a consistent CBF. We estimate this response using CVCi, and show at the beginning of exercise, CVCi increases (suggesting vasodilation) towards CVCI M , then settles back towards baseline where it remains for the duration of the exercise bout. Characterizing this initial phase as a delay masks this large vascular activity. The ability of cerebral vessels to autoregulate and maintain blood flow has been well established in the development of neurodegenerative diseases (Claassen et al., 2021). Therefore, these initial oscillations should be more closely investigated instead of being attributed to a TD (Billinger et al., 2017;Weston et al., 2022;Witte et al., 2019). We suggest that analysis of MCAv dynamics should be accompanied by an analysis of CPP to better understand the myriad of physiological changes occurring. Interpretation of MCAv kinetic variables without an understanding of CPP is difficult. For instance, TD appears to be slightly altered following a stroke compared to healthy adults (Billinger et al., 2021;Ward et al., 2018) and quicker following kidney transplants (Ward et al., 2022). The utility of a monoexponential model to characterize the transition of MCAv from rest-to-exercise is apparent. Such a model may help determine mechanistic contributions to CBF and aid earlier detection of maladaptive responses, leading to earlier treatment. However, before conclusions can be drawn, a better understanding of what these variables are telling us is warranted.